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Title: Development of multi variant feature based on mammographic image classification using k means clustering
Researcher: S julian Savari Antony
Guide(s): S.Ravi
Keywords: Electronics And Communication Engineering
University: Shri Jagdishprasad Jhabarmal Tibarewala University
Completed Date: 07/07/2015
Abstract: newline
Appears in Departments:Faculty of Engineering

Files in This Item:
File Description SizeFormat 
01_title page.pdfAttached File31.2 kBAdobe PDFView/Open
02_declaration.pdf18.66 kBAdobe PDFView/Open
03_certificate.pdf19.08 kBAdobe PDFView/Open
04_acknowledgement.pdf19.07 kBAdobe PDFView/Open
05_table of contents.pdf29.47 kBAdobe PDFView/Open
06_list of tables.pdf42.26 kBAdobe PDFView/Open
07_list of figures.pdf31.96 kBAdobe PDFView/Open
08_list of abbreviations.pdf22.53 kBAdobe PDFView/Open
09_abstract.pdf26.14 kBAdobe PDFView/Open
10_chapter 1 introduction.pdf229.04 kBAdobe PDFView/Open
11_chapter 2 review of literature.pdf175.8 kBAdobe PDFView/Open
12_chapter 3 development of efficient image quarrying technique for mammographic image classificatio.pdf351.47 kBAdobe PDFView/Open
13_chapter 4 a knowledge-based inferences system for the early detection of breast cancer.pdf650.2 kBAdobe PDFView/Open
14_chapter 5 detection of masses in digital mammograms using neural network.pdf1.56 MBAdobe PDFView/Open
15_chapter 6 development of efficient automatic volumetric mammographic image classification based o.pdf402.97 kBAdobe PDFView/Open
16_chapter 7 graph based density estimation for mammogram classification based on gray values.pdf226.76 kBAdobe PDFView/Open
17_chapter 8 region centric mass calibration technique for mammogram classification using gabor filt.pdf113.08 kBAdobe PDFView/Open
18_chapter 9 small scale cubic mass estimation technique based k-means clustering approach for mammo.pdf159.46 kBAdobe PDFView/Open
19_chapter x summary and conclusion.pdf186.16 kBAdobe PDFView/Open
20_chapter xi references.pdf59.27 kBAdobe PDFView/Open
21_published papers.pdf39.46 kBAdobe PDFView/Open

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